Interactive Research Articles

Considering communication features of English-language argumentative interaction in the BBC 1 documentary series “Fake or Fortune”

Considering communication features of English-language argumentative interaction in the BBC 1 documentary series “Fake or Fortune”

Deep Learning-Based Intelligent Detection Algorithm for Surface Disease in Concrete Buildings

In this study, the extent of concrete building distress is used to determine whether a building needs to be demolished and maintained, and the study focuses on accurately identifying target distress in different complex contexts and accurately distinguishing between their categories. To solve the problem of insufficient feature extraction of small targets in bridge disease images under complex backgrounds and noise, we propose the YOLOv8 Dynamic Plus model. First, we enhanced attention on multi-scale disease features by implementing structural reparameterization with parallel small-kernel expansion convolution. Next, we reconstructed the relationship between localization and classification tasks in the detection head and implemented dynamic selection of interactive features using a feature extractor to improve the accuracy of classification and recognition. Finally, to address problems of missed detection, such as inadequate extraction of small targets, we extended the original YOLOv8 architecture by adding a layer in the feature extraction phase dedicated to small-target detection. This modification integrated the neck part more effectively with the shallow features of the original three-layer YOLOv8 feature extraction stage. The improved YOLOv8 Dynamic Plus model demonstrated a 7.4 percentage-point increase in performance compared to the original model, validating the feasibility of our approach and enhancing its capability for building disease detection. In practice, this improvement has led to more accurate maintenance and safety assessments of concrete buildings and earlier detection of potential structural problems, resulting in lower maintenance costs and longer building life. This not only improves the safety of buildings but also brings significant economic benefits and social value to the industries involved.

SciSpace for Finding Relevant Literature in English Language Education Contexts: A Technology Review

Writing a literature review part might be a challenging task for university students as they have to sit for some time to find recent literature that is closely relevant to their research topics. In that case, the authors spot the potential of utilizing SciSpace, Artificial Intelligence technology, to support the students in their literature review activities. In this study, the authors aim to review the potential benefits and challenges of using SciSpace to find recent literature relevant to students’ research topics, framed under the task engagement principles. To achieve that study goal, the authors used a recent technology review approach to do the review and conducted rigorous peer debriefing activities to ensure that the review results are credible and trustworthy. The review results showed the benefits of using SciSpace, which included fostering task authenticity, maintaining students’ interest, facilitating social interaction, supporting autonomy, and providing effective scaffolding. Meanwhile, the challenges of using SciSpace involved content accuracy and relevance, updating tailored content, handling complex queries and visuals, and offering context-specific feedback. Recommendations for maximizing the application's potential include improving content filtering, expanding interactive features, and providing specialized feedback. Future research agendas also are presented.

ACA-Net: adaptive context-aware network for basketball action recognition.

The advancements in intelligent action recognition can be instrumental in developing autonomous robotic systems capable of analyzing complex human activities in real-time, contributing to the growing field of robotics that operates in dynamic environments. The precise recognition of basketball players' actions using artificial intelligence technology can provide valuable assistance and guidance to athletes, coaches, and analysts, and can help referees make fairer decisions during games. However, unlike action recognition in simpler scenarios, the background in basketball is similar and complex, the differences between various actions are subtle, and lighting conditions are inconsistent, making action recognition in basketball a challenging task. To address this problem, an Adaptive Context-Aware Network (ACA-Net) for basketball player action recognition is proposed in this paper. It contains a Long Short-term Adaptive (LSTA) module and a Triplet Spatial-Channel Interaction (TSCI) module to extract effective features at the temporal, spatial, and channel levels. The LSTA module adaptively learns global and local temporal features of the video. The TSCI module enhances the feature representation by learning the interaction features between space and channels. We conducted extensive experiments on the popular basketball action recognition datasets SpaceJam and Basketball-51. The results show that ACA-Net outperforms the current mainstream methods, achieving 89.26% and 92.05% in terms of classification accuracy on the two datasets, respectively. ACA-Net's adaptable architecture also holds potential for real-world applications in autonomous robotics, where accurate recognition of complex human actions in unstructured environments is crucial for tasks such as automated game analysis, player performance evaluation, and enhanced interactive broadcasting experiences.

MmSTCT: spatial–temporal convolution transformer network considering driving intention for multimodal vehicle trajectory prediction of highway

Due to the dynamic nature of traffic flow and the uncertainty of drivers' maneuvers, multimodal trajectory prediction remains challenging. In this paper, a multimodal vehicle trajectory prediction model is proposed based on the encoder-decoder structure, utilizing a spatial-temporal dilated causal convolutional transformer network for the hierarchical extraction of driving intention and spatial interaction features. Specifically, temporal information is extracted from the initial motion states by utilizing a bi-directional long short-term memory network. Subsequently, the social interaction module and spatial-temporal dependency module are introduced to model the interactive influences among surrounding vehicles and the target vehicle at the same timestamp as well as at different timestamps. Then adaptive fusion of driving intention is performed to generate multiple queries for accommodating the output of multimodal trajectory prediction module in a non-autoregressive manner. The effectiveness of the proposed model is demonstrated through experimental evaluations on the NGSIM, HighD and CitySim datasets.

Interactional Competence Instruction for Beginner KFL Learners

ABSTRACT Interactional competence (IC) refers to the ability to use interactional resources, such as turn-taking, sequencing, overall structuring, and repair during actual interaction (Wong & Waring, 2021). Language learners must develop IC to engage in conversation using the target language (TL) in culturally appropriate and interactive ways. However, teaching IC is often overlooked, especially for beginner learners. In response to this need, this article presents an interaction-oriented project called the “Conversation Project” recently implemented in a first semester Korean as a foreign language (KFL) college course. In this project, KFL learners are explicitly taught a set of interactional features of Korean through authentic learning material and then given opportunities to apply them in a social and immersive learning environment with Korean speakers. The project aims to foster IC development and enhance learners’ conversational skills in real-life intercultural situations.

Development of Interactive Learning Media Based on an Articulate Storyline in Grade fourth of Elementary School

On August 11st, 2022, In the ever-evolving digital era, the utilization of technology for educational purposes is becoming increasingly important. One of the latest innovations is the development of an Android app-based English learning media specifically designed to introduce basic concepts about three types of animals based on their food types. This study evaluates the impact of Articulate Storyline-based interactive learning media on grade 4 students. The results showed that the use of this media provided a number of benefits for students. They did not feel bored during the learning process and were able to distinguish three types of animals based on their food. This media also encourages independent and explorative learning, increases student engagement through interactive features, and helps teachers deliver material more effectively. Therefore, the use of this interactive learning media can be an effective solution to improve the quality of learning at the primary school level This app not only serves as a language learning tool, but also as an educational tool that invites users to understand the differences between herbivores, carnivores and omnivores in an interactive and engaging way. Through interactive features such as exercises and quizzes, users can access this learning material easily on their Android devices, making the learning process more effective and fun.

Human Mesofluidic Intestinal Model for Studying Transport of Drug Carriers and Bacteria Through a Live Mucosal Barrier.

The intestinal mucosal barrier forms a critical interface between lumen contents such as bacteria, drugs, and drug carriers and the underlying tissue. Current in vitro intestinal models, while recapitulating certain aspects of this barrier, generally present challenges with respect to imaging transport across mucus and uptake into enterocytes. A human mesofluidic small intestinal chip was designed to enable facile visualization of a mucosal interface created by growing primary human intestinal cells on a vertical hydrogel wall separating channels representing the intestinal lumen and circulatory flow. Type I collagen, fortified via cross-linking to prevent deformation and leaking during culture, was identified as a suitable gel wall material for supporting primary organoid-derived human duodenal epithelial cell attachment and monolayer formation. Addition of DAPT and PGE2 to culture medium paired with air-liquid interface culture increased the thickness of the mucus layer on epithelium grown within the device for 5 days from approximately 5 mm to 50 μm, making the model suitable for revealing intriguing features of interactions between luminal contents and the mucus barrier using live cell imaging. Time-lapse imaging of nanoparticle diffusion within mucus revealed a zone adjacent to the epithelium largely devoid of nanoparticles up to 4.5 hr after introduction to the lumen channel, as well as pockets of dimly lectin-stained mucus within which particles freely diffused, and apparent clumping of particles by mucus components. Multiple particle tracking conducted on the intact mucus layer in the chip revealed significant size-dependent differences in measured diffusion coefficients. E. coli introduced to the lumen channel were freely mobile within the mucus layer and appeared to intermittently contact the epithelial surface over 30 minute periods of culture. Mucus shedding into the lumen and turnover of mucus components within cells were visualized. Taken together, this system represents a powerful tool for visualization of interactions between luminal contents and an intact live mucosal barrier.

Vehicle trajectory prediction based on cross-attention and multilevel spatio-temporal features

Accurately predicting vehicle trajectories is crucial for motion planning in autonomous driving. Some existing trajectory prediction models employ Long Short-Term Memory (LSTM) networks to encode trajectory information, but this approach may lead to information loss. Additionally, the sparsity of the grid representation may limit the model’s ability to extract vehicle interaction features. To address these issues, this paper proposes a vehicle trajectory prediction model based on cross-attention and multilevel spatio-temporal features. The proposed model incorporates a spatio-temporal feature extraction module designed to capture the dynamic evolution of vehicle states across both time and space. Furthermore, it employs a temporal cross-attention module that reuses hidden states to select spatio-temporal features closely related to the target vehicle’s historical state, thus compensating for the temporal information loss during the encoding process. Additionally, a spatial cross-attention module, informed by social context, is deployed to analyze the dynamic interactions among vehicles. This module filters spatio-temporal interaction features with a significant impact on the target vehicle’s trajectory. The model is trained and tested using the NGSIM dataset and the highD dataset. Compared with other models, the model presented in this paper shows higher prediction accuracy in long-term trajectory prediction.

NGHIÊN CỨU TỔNG QUAN VIỆC SỬ DỤNG ỨNG DỤNG TRÍ TUỆ NHÂN TẠO TRONG VIỆC XÂY DỰNG VIDEO HỌC LIỆU SỐ

Developing digital learning materials, especially video lectures, is becoming an important issue in education. The emergence of artificial intelligence (AI) has made the creation of videos easier compared to the traditional approach. However, the education sector has yet to benefit from these advanced technologies fully. This research explores the application of Artificial Intelligence (AI) in creating digital learning videos. The study uses 43 selected articles and the PRISMA analysis model to search, classify, and content-filter the results. The findings show a noticeable growth in using AI to create digital learning videos, with 33 topics formed from keywords and articles published in various journals. The most influential works include automatic content extraction from videos and virtual teachers, and there is significant interest in ChatGPT. The main research topics include • developing effective AI models to convert text to video, • integrating personalization and interaction features, and • applying these digital learning videos in education and training. However, many issues remain to be researched, such as enhancing the context-understanding capabilities of AI models and building frameworks to evaluate the effectiveness of videos. AI-generated videos' quality, reliability, and flexibility are significant unresolved challenges, opening up research gaps for future scholars. Keywords: Artificial Intelligence (AI); video; education; learning; PRISMA.

Strategi Komunikasi Pemasaran dalam Optimalisasi Media Sosial Instagram Nafas Pertama

This research aims to identify and analyze First Breath marketing communication strategies in optimizing Instagram. First Breath, a birth documentation vendor, uses Instagram to expand its market and increase interaction with its audience. Using Integrated Marketing Communications (IMC) theory, this research focuses on advertising, sales promotions, public relations, direct marketing, interactive marketing, events and experiences, and word of mouth marketing. The research method is a qualitative case study through in-depth interviews with the marketing team, Instagram content analysis, and participant observation. The research results show that First Breath's communication strategy includes attractive visuals, collaboration with influencers, educational and inspirational content, as well as the use of Instagram's interactive features such as Stories and Reels. Consistency in branding and messaging is also important for a positive image and customer loyalty. In conclusion, optimizing Instagram through an integrated marketing communications strategy increases audience engagement and expands market reach. This research provides insight into best practices in utilizing social media for effective marketing communications, as well as recommendations for similar companies in developing innovative and efficient digital communications strategies.

Research on Personalized Education Intervention Strategy Based on Barrage Emotional Analysis

With the development of online teaching, the interactive feature of bullet comments has increasingly highlighted its potential in reflecting learners' emotional attitudes. This study employs bullet comment sentiment analysis as a tool to explore its application in personalized educational intervention. By collecting and processing bullet comment data and utilizing cutting-edge sentiment analysis techniques, the study aims to uncover learners' cognitive and emotional needs. Furthermore, this paper analyzes educational intervention strategies and focuses on the specific needs of personalized education. Through the construction of a scientific empirical research model, the effectiveness and practicality of sentiment analysis of bullet comments in personalized intervention strategies have been validated. Experimental results support the research hypothesis, indicating that sentiment analysis based on bullet comments plays a significant role in enhancing the personalization of educational intervention strategies. The conclusions of this study contribute to optimizing the online teaching environment and improving educational quality and efficiency.

Microphysics of shock-grain interaction for inertial confinement fusion ablators in a fluid approach.

Ablator materials used for inertial confinement fusion, such as high-density carbon (HDC) and beryllium, have grain structure which may lead to small-scale density nonuniformity and the generation of perturbations when the materials are shocked and compressed. Here, we use a combination of a linear theory of shock interaction with density nonuniformity [Velikovich et al., Phys. Plasmas 14, 072706 (2007)10.1063/1.2745809] and numerical simulations to study shock interaction with a model representation of HDC grains. While the shock-grain interaction is nonlinear, the linear theory shows some key features of the shock-grain interaction, which also hold for the (nonlinear) simulations. The postshock perturbations are made up of sonic reflections off of grain boundaries and vorticity deposition along them, with the latter dominating the perturbed energy content. The mean (per mass) postshock perturbed kinetic energy decreases with increasing grain size, but energy will be deposited at increasing spatial scale. From the perspective of the postshock perturbed energy, the detailed linear theory largely supports a proposed method [S. Davidovits et al., Phys. Plasmas 29, 112708 (2022)1070-664X10.1063/5.0107534] for deresolving the grains (in a similar grains model) that treats the grains statistically. Our simulation results highlight the influence of thermal conduction on the perturbation dynamics at grain scales.

Vehicle trajectory prediction method integrating spatiotemporal relationships with hybrid time-step scene interaction

In vehicle trajectory prediction, constructing the interactive relationships among vehicles within the traffic environment poses a significant challenge. Existing models predominantly focus on temporal dependencies within vehicle histories and spatial correlations among neighboring vehicles, overlooking the continuous influence of historical vehicle states on the current time step and the interplay of multiple sequences over time. To address these limitations, we propose a method for multimodal vehicle trajectory prediction that integrates Hybrid Time-step Scene Interaction (HTSI) into the spatiotemporal relationships. Firstly, we introduce the HTSI module, comprising Multi-step Temporal Information Aggregation (MTIA) and Single-step Temporal Information Aggregation (STIA) methods. MTIA utilizes multi-head attention mechanisms to capture temporal dependencies between consecutive frames, thereby generating new time series amalgamating the ongoing influence of historical time states on the current timestamp. Simultaneously, STIA employs multi-head attention mechanisms to capture the spatial dimension weights of multiple time series and, by aggregating spatial interaction features at each timestamp, generates new time series fused with spatial interaction influences. Subsequently, feature extraction is performed through LSTM layers. Moreover, we propose an improved DIPM pooling module, improving the model’s long-term prediction capability by selectively reusing historical hidden states. Ultimately, based on training results from the HighD and NGSIM datasets, our model demonstrates significant advantages in long-term prediction compared to other state-of-the-art trajectory prediction models. Specifically, within the 5 s prediction window, the model achieved a root mean square error (RMSE) of 2.79 m on the NGSIM dataset, representing a 33.62% improvement over the baseline model’s average accuracy. Additionally, on the HighD dataset, the model attained an RMSE of 2.16 m, reflecting a 33.43% enhancement. The crucial code can be obtained from the provided link: https://github.com/gyhhq/Prediction-trajectory .

Assessment of Eye Care Apps for Children and Adolescents Based on the Mobile App Rating Scale: Content Analysis and Quality Assessment.

In China, the current situation of myopia among children and adolescents is very serious. Prevention and control of myopia are inhibited by the lack of medical resources and the low awareness about eye care. Nevertheless, mobile apps provide an effective means to solve these problems. Since the health app market in China is still immature, it has become particularly important to conduct a study to assess the quality of eye-care apps to facilitate the development of better eye-care service strategies. This study aimed to evaluate the quality, functionality, medical evidence, and professional background of eye-care apps targeting children and adolescents in the Chinese app stores. A systematic search on iOS and Android app stores was performed to identify eye-care apps for children and adolescents. The general characteristics, development context, and functional features of the apps were described. Quality assessment of the apps was completed by 2 independent researchers using the Mobile App Rating Scale. This study included 29 apps, of which 17 (59%) were developed by commercial organizations and 12 (41%) had a design with relevant scientific basis. The main built-in functions of these apps include self-testing (18/29, 62%), eye exercises (16/29, 55%), and eye-care education (16/29, 55%). The mean overall quality of eye-care apps was 3.49 (SD 0.33), with a score ranging from 2.89 to 4.39. The overall Mobile App Rating Scale score exhibited a significant positive correlation with the subscale scores (r=0.81-0.91; P<.001). In addition, although most apps provided basic eye-care features, there are some deficiencies. For example, only a few apps were developed with the participation of medical organizations or professional ophthalmologists, and most of the apps were updated infrequently, failing to provide the latest eye-care information and technology in a timely manner. In general, the quality of eye-care apps for children and teenagers in Chinese app stores is good. These apps fulfill users' needs for eye-care services to a certain extent, but they still suffer from insufficient medical background, low user engagement, and untimely updates. In order to further improve the effectiveness of eye-care apps, cooperation with medical institutions and professional ophthalmologists should be strengthened to enhance the scientific and authoritative nature of the apps. At the same time, interactive features and regular updates should be added to enhance user participation and the continuity of the apps. This study provides a reference for future development or improvement of eye-care apps, which can help promote myopia prevention and control.

NARRATIVE MULTIMODAL TEXT AS A TYPE OF MULTIMODAL TEXT

The modern communication environment is saturated with multimodal information with which a person actively interacts in everyday life. Researchers of multimodal texts have proven that the perception of these texts differs from the perception of linear monomodal texts, since the semantic load in the multimodal text is distributed between verbal and non-verbal modes. However, in addition to the multimodality itself, such texts may have other features, for example, narrative. Currently, quite a few works are devoted to the study of narrative multimodal texts in general, as a type of multimodal texts. In our work, we pay attention to narrative multimodal texts. The article analyses the concept of narrative multimodal text, provides a classification of these texts. Features of interaction of narrative and multimodality within one text are considered, influence of narrative on correlation of modes in the text is revealed, as well as influence of two given features on perception of such texts. The issue of further study of narrative and multimodality interaction within one text is actualised.

Run for the group: Examining the effects of group-level social interaction features of fitness apps on exercise participation

Mobile fitness apps have gained popularity as a way of improving healthy behaviors. This study investigates the design and influence of fitness apps from a novel “group” perspective, focusing on the effects of group-level social interaction features in exercise participation. Through empirical studies on a unique dataset, we propose and verify a conceptual framework that shows that (1) group role model inspiration and group comparison features facilitate the intra-group assimilation and inter-group contrast processes and further significantly improve group participation, and that (2) offline activities moderate the effects of group comparison features on group participation. This study contributes to the literature on online communities and fitness apps/service design by demonstrating the motivating power of group-level interactions and online–offline engagement. It also provides practical guidelines for fitness app developers.

Purchase intention toward E-pharmacy: the consumption value perspective

Purpose This study aims to examine consumer purchase behavior towards E-pharmacy from a consumption value perspective. For this purpose, the influence of consumption values on consumer purchase intention is evaluated. Further, the role of consumer involvement in online purchase setup in the context of E-pharmacy is observed using moderation analysis. Design/methodology/approach The study used a cross-sectional survey design, wherein the respondents in the Delhi-National Capital Region of India were approached using a purposive sampling method. Responses received through the structured questionnaire were subjected to analysis using Smart PLS Version 4.0.9.6. Findings Results indicate a significant influence of functional (partial), emotional, social and conditional values on consumers’ purchase intention. Moreover, the moderation effect of consumer online involvement is seen in functional value (partial) and emotional value–purchase intention relationships. Research limitations/implications This study reinforces the use of the consumption value perspective to explain consumer purchase behavior toward information system (IS)-based platforms such as E-pharmacy. Some of the results, in this context, that did not establish significant relationships between a given form of consumption values and purchase intention, open up the possibilities of retesting the said relationships in the future. The researchers would realize the benefit of such examinations, as the E-pharmacy sector further evolves and matures in the future. Further, the successful establishment of a significant moderating role of consumer online involvement in certain consumption value-purchase intention relationships such as safety, perceived convenience and comfort value paves the way for future researchers to explore consumer online involvement for a similar role in IS research. Practical implications This study provides cues for E-pharmacy marketers to focus on enhancing product safety, usage comfort, users’ social image and awareness toward health consciousness that will help build purchase intention and assist in overcoming the challenges in the long run. It further suggests that marketers should have a strong re-look at forming price value perceptions and they must enhance the interactive features of the company’s mobile apps/websites with the help of the emerging artificial intelligence tools to re-orient the epistemic value and perceived convenience value toward E-pharmacy services. Furthermore, this study recommends following digital marketing practices to increase consumer online involvement, which would help strengthen perceived convenience formation that would otherwise be difficult to attain for E-pharmacy consumers. Originality/value This study provides novel insight into consumer purchase intention evaluation through the consumption value perspective in an emerging E-pharmacy market. The consumption value frame of reference helps to understand consumers’ buying rationale that affects their buying behavior beyond the initial adoption. Further, the moderation analysis of consumer online involvement provides an additional dimension to understanding consumer buying behavior concerning E-pharmacy.

CSLens: Towards Better Deploying Charging Stations via Visual Analytics -- A Coupled Networks Perspective.

In recent years, the global adoption of electric vehicles (EVs) has surged, prompting a corresponding rise in the installation of charging stations. This proliferation has underscored the importance of expediting the deployment of charging infrastructure. Both academia and industry have thus devoted to addressing the charging station location problem (CSLP) to streamline this process. However, prevailing algorithms addressing CSLP are hampered by restrictive assumptions and computational overhead, leading to a dearth of comprehensive evaluations in the spatiotemporal dimensions. Consequently, their practical viability is restricted. Moreover, the placement of charging stations exerts a significant impact on both the road network and the power grid, which necessitates the evaluation of the potential post-deployment impacts on these interconnected networks holistically. In this study, we propose CSLens, a visual analytics system designed to inform charging station deployment decisions through the lens of coupled transportation and power networks. CSLens offers multiple visualizations and interactive features, empowering users to delve into the existing charging station layout, explore alternative deployment solutions, and assess the ensuring impact. To validate the efficacy of CSLens, we conducted two case studies and engaged in interviews with domain experts. Through these efforts, we substantiated the usability and practical utility of CSLens in enhancing the decision-making process surrounding charging station deployment. Our findings underscore CSLens's potential to serve as a valuable asset in navigating the complexities of charging infrastructure planning.

Human Computer Interaction Strategies for Effective Digital Learning Experiences: From Classroom to Screen

The rapid advancement of technology has transformed traditional classroom settings into complex digital learning environments, emphasizing the need for effective Human-Computer Interaction (HCI) strategies to enhance online learning experiences. This research explores the intersection of HCI and digital learning, focusing on how HCI strategies can improve user engagement, comprehension, and learning outcomes. Through a review of existing literature, this study identifies common challenges faced by learners and educators, such as usability issues, technical difficulties, and inadequate localization. It also highlights effective HCI strategies, including user- centered design, interactive multimedia features, and feedback mechanisms, which are crucial for creating engaging and personalized learning experiences. Findings indicate that personalized learning systems and interactive content significantly enhance performance and achievement by addressing individual learning needs and improving content retention. Best practices derived from user feedback suggest adopting a user-centered design approach, integrating interactive elements, focusing on usability, and continuously updating learning tools. This research underscores the importance of aligning HCI principles with educational technology to foster a more effective and inclusive digital learning environment. Keywords - Human-Computer Interaction (HCI), Digital Learning Environments, User Experience (UX),Interactive Design, Personalized Learning, Educational Technology.